Memory OS AI

io.github.romainsantoli-web

Adaptive memory for AI agents — FAISS search, chat extraction, cross-project linking

Add to Cursor Add to VS Code Run on PlugMCP

1{2  "mcpServers": {3    "io.github.romainsantoli-web-memory-os-ai": {4      "command": "uvx",5      "args": [6        "memory-os-ai==3.0.1"7      ]8    }9  }10}

.cursor/mcp.jsonMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx memory-os-ai==3.0.1

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1claude mcp add io.github.romainsantoli-web-memory-os-ai -- uvx memory-os-ai==3.0.1

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.romainsantoli-web-memory-os-ai": {4      "command": "uvx",5      "args": [6        "memory-os-ai==3.0.1"7      ]8    }9  }10}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version: 3.0.1
Last checked: 2026-10-28

Stars: —
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Memory OS AI?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Memory OS AI need an API key?

No required environment variable is listed on this record.

What transport does Memory OS AI use?

Memory OS AI runs locally over stdio.

When was Memory OS AI last checked?

Last checked 2026-10-28. Adaptive memory for AI agents — FAISS search, chat extraction, cross-project linking

From the publisher

Documentation

Memory OS AI

Adaptive memory system for AI agents — universal MCP server for Claude Code, Codex CLI, VS Code Copilot, ChatGPT, and any MCP-compatible client.

Concept

Memory OS AI transforms your local documents (PDF, DOCX, images, audio) into a semantic memory queryable by any AI model through the MCP (Model Context Protocol).

┌──────────────────────────────────┐
│  AI Client (any MCP-compatible)  │
│  Claude Code / Codex / Copilot   │
│  ChatGPT / custom agents         │
├──────────────────────────────────┤
│         MCP Protocol             │
│   stdio / SSE / Streamable HTTP  │
├──────────────────────────────────┤
│      Memory OS AI Server         │
│  ┌────────┐  ┌───────────────┐   │
│  │ FAISS  │  │ Chat Extractor│   │
│  │ Index  │  │ (4 sources)   │   │
│  └────────┘  └───────────────┘   │
│  ┌────────────────────────────┐  │
│  │ Cross-Project Linking      │  │
│  └────────────────────────────┘  │
└──────────────────────────────────┘

Features

21 MCP tools for memory management, search, chat persistence, project linking, and cloud storage
Semantic search with FAISS + SentenceTransformers (all-MiniLM-L6-v2)
Multi-format ingestion: PDF, DOCX, TXT, images (OCR), audio (Whisper), PPTX
Chat extraction: auto-detects Claude, ChatGPT, Copilot, and terminal history
Cross-project linking: share memory across multiple workspaces
Cloud storage overflow: auto-backup to Google Drive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2
3 transports: stdio (default), SSE (--sse), Streamable HTTP (--http)
MCP Resources: memory://documents/*, memory://logs/conversation, memory://linked/*
Local-first: all data on your machine by default, cloud only when disk runs low

21 MCP Tools

Tool	Description
`memory_ingest`	Index a folder of documents into FAISS
`memory_search`	Semantic search across all indexed content
`memory_search_occurrences`	Count keyword occurrences across documents
`memory_get_context`	Get relevant context for the current task
`memory_list_documents`	List all indexed documents with stats
`memory_transcribe`	Transcribe audio files (Whisper)
`memory_status`	Engine status (index size, model, device)
`memory_compact`	Compact/deduplicate the FAISS index
`memory_chat_sync`	Sync messages from configured chat sources
`memory_chat_source_add`	Add a chat source (Claude, ChatGPT, etc.)
`memory_chat_source_remove`	Remove a chat source
`memory_chat_status`	Status of all chat sources
`memory_chat_auto_detect`	Auto-detect chat workspaces on disk
`memory_session_brief`	Full memory briefing for session start
`memory_chat_save`	Persist conversation messages to memory
`memory_project_link`	Link another project's memory
`memory_project_unlink`	Unlink a project
`memory_project_list`	List all linked projects
`memory_cloud_configure`	Configure cloud storage backend for overflow
`memory_cloud_status`	Show local disk + cloud storage status
`memory_cloud_sync`	Push/pull/auto-sync between local and cloud

Quick Start

Prerequisites

Python 3.10+
Optional: tesseract (OCR), ffmpeg (audio), antiword (legacy .doc)

# macOS
brew install tesseract ffmpeg antiword

# Ubuntu/Debian
sudo apt-get install tesseract-ocr ffmpeg antiword

Install

git clone https://github.com/romainsantoli-web/Memory-os-ai.git
cd Memory-os-ai
pip install -e ".[dev,audio]"

Auto-Setup (recommended)

# Setup for your AI client:
memory-os-ai setup claude-code    # Claude Code
memory-os-ai setup codex          # Codex CLI
memory-os-ai setup vscode         # VS Code Copilot
memory-os-ai setup claude-desktop # Claude Desktop
memory-os-ai setup chatgpt        # ChatGPT (manual bridge)
memory-os-ai setup all            # All of the above

# Check status:
memory-os-ai setup status

Manual Start

# stdio (default — Claude Code, VS Code, Codex)
memory-os-ai

# SSE transport (port 8765)
memory-os-ai --sse

# Streamable HTTP (port 8765)
memory-os-ai --http

Project Structure

Memory-os-ai/
├── src/memory_os_ai/
│   ├── __init__.py          # Public API: MemoryEngine, ChatExtractor, TOOL_MODELS
│   ├── __main__.py          # python -m memory_os_ai entry point
│   ├── server.py            # MCP server — 21 tools, 3 transports, resources
│   ├── engine.py            # FAISS engine — indexing, search, compact, session brief
│   ├── cloud_storage.py     # 8 cloud backends (GDrive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2)
│   ├── storage_router.py    # Smart routing: local-first with cloud overflow
│   ├── models.py            # 21 Pydantic models + TOOL_MODELS registry
│   ├── chat_extractor.py    # 4 extractors: Claude, ChatGPT, Copilot, terminal
│   ├── instructions.py      # MEMORY_INSTRUCTIONS for AI clients
│   └── setup.py             # Auto-setup CLI for 5 AI clients
├── bridges/
│   ├── claude-code/         # CLAUDE.md with memory rules
│   ├── claude-desktop/      # config.json for Claude Desktop
│   ├── codex/               # AGENTS.md for Codex CLI
│   ├── vscode/              # mcp.json for VS Code
│   └── chatgpt/             # mcp-connection.json for ChatGPT
├── tests/                   # 410+ tests — 96% coverage
│   ├── test_memory.py       # Engine + models (60 tests)
│   ├── test_chat_extractor.py  # Chat extraction (39 tests)
│   ├── test_bridges.py      # Bridge configs (22 tests)
│   ├── test_gaps.py         # Compact, cross-project, resources (34 tests)
│   ├── test_server_dispatch.py # Server dispatch + async (61 tests)
│   ├── test_setup.py        # Setup CLI targets
│   ├── test_z_coverage_boost.py # Coverage boost (35 tests)
│   └── test_zz_full_coverage.py # Full coverage (97 tests)
├── pyproject.toml           # v3.1.0 — deps, scripts, coverage config + cloud optional deps
├── Dockerfile               # Container deployment
└── README.md

Cloud Storage (v3.1.0)

When local disk runs low (< 500 MB free by default), memory data automatically overflows to a configured cloud backend.

Supported Providers

Provider	Install	Credentials
Google Drive	`pip install memory-os-ai[cloud-gdrive]`	`credentials_json` or `token_json` + `folder_id`
iCloud Drive	(macOS native, no extra deps)	`container` name (default: `memory-os-ai`)
Dropbox	`pip install memory-os-ai[cloud-dropbox]`	`access_token` + `folder`
OneDrive	(auto-detects mount) or Graph API	`mount_path` or `access_token`
Amazon S3	`pip install memory-os-ai[cloud-s3]`	`bucket`, `aws_access_key_id`, `aws_secret_access_key`
Azure Blob	`pip install memory-os-ai[cloud-azure]`	`connection_string` + `container`
Box	`pip install memory-os-ai[cloud-box]`	`access_token` + `folder_id`
Backblaze B2	`pip install memory-os-ai[cloud-b2]`	`application_key_id`, `application_key`, `bucket_name`
All providers	`pip install memory-os-ai[cloud-all]`	—

Usage

# Configure via environment (auto-activates on server start)
export MEMORY_CLOUD_PROVIDER=icloud
export MEMORY_CLOUD_CONFIG='{"container": "memory-os-ai"}'
memory-os-ai

# Or configure at runtime via MCP tool:
#   memory_cloud_configure(provider="s3", credentials={"bucket": "my-bucket", ...})
#   memory_cloud_status()       → local disk + cloud usage
#   memory_cloud_sync("push")   → backup to cloud
#   memory_cloud_sync("pull")   → restore from cloud
#   memory_cloud_sync("auto")   → offload if disk low

Configuration

Environment Variables

Variable	Default	Description
`MEMORY_CACHE_DIR`	`~/.memory-os-ai`	Cache / FAISS index directory
`MEMORY_MODEL`	`all-MiniLM-L6-v2`	SentenceTransformer model name
`MEMORY_API_KEY`	(none)	Optional API key for SSE/HTTP auth
`MEMORY_CLOUD_PROVIDER`	(none)	Cloud provider name (see table above)
`MEMORY_CLOUD_CONFIG`	(none)	JSON credentials or path to JSON file
`MEMORY_DISK_THRESHOLD`	`524288000`	Bytes free before cloud overflow (500 MB)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Run with coverage
python -m pytest tests/ --cov=memory_os_ai --cov-report=term-missing

# Coverage threshold: 80% (enforced in pyproject.toml)

License

GNU Lesser General Public License v3.0 (LGPL-3.0). See LICENSE for details.

For commercial licensing, contact [email protected].

Part of the OpenClaw Ecosystem

Memory OS AI is designed to work alongside the OpenClaw agent infrastructure:

Repo	Description
setup-vs-agent-firm	Factory for AI agent firms — 28 SKILL.md, 5 SOUL.md, 15 sectors
mcp-openclaw-extensions	115 MCP tools — security audit, A2A bridge, fleet management
Memory OS AI (this repo)	Semantic memory + chat persistence — universal MCP bridge

Together they form a complete stack: memory (this repo) → skills & souls (setup-vs-agent-firm) → security & orchestration (mcp-openclaw-extensions).

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

⚠️ Contenu généré par IA — validation humaine requise avant utilisation.

Memory OS AI

io.github.romainsantoli-web

Adaptive memory for AI agents — FAISS search, chat extraction, cross-project linking

Knowledge Memory

Add to Cursor Add to VS Code Run on PlugMCP

GitHub

Add to Cursor Add to VS Code Run on PlugMCP

1{2  "mcpServers": {3    "io.github.romainsantoli-web-memory-os-ai": {4      "command": "uvx",5      "args": [6        "memory-os-ai==3.0.1"7      ]8    }9  }10}

.cursor/mcp.jsonMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx memory-os-ai==3.0.1

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1claude mcp add io.github.romainsantoli-web-memory-os-ai -- uvx memory-os-ai==3.0.1

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.romainsantoli-web-memory-os-ai": {4      "command": "uvx",5      "args": [6        "memory-os-ai==3.0.1"7      ]8    }9  }10}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version: 3.0.1
Last checked: 2026-10-28

Stars: —
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Memory OS AI?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Memory OS AI need an API key?

No required environment variable is listed on this record.

What transport does Memory OS AI use?

Memory OS AI runs locally over stdio.

When was Memory OS AI last checked?

Last checked 2026-10-28. Adaptive memory for AI agents — FAISS search, chat extraction, cross-project linking

From the publisher

Documentation

Memory OS AI

Adaptive memory system for AI agents — universal MCP server for Claude Code, Codex CLI, VS Code Copilot, ChatGPT, and any MCP-compatible client.

Concept

Memory OS AI transforms your local documents (PDF, DOCX, images, audio) into a semantic memory queryable by any AI model through the MCP (Model Context Protocol).

┌──────────────────────────────────┐
│  AI Client (any MCP-compatible)  │
│  Claude Code / Codex / Copilot   │
│  ChatGPT / custom agents         │
├──────────────────────────────────┤
│         MCP Protocol             │
│   stdio / SSE / Streamable HTTP  │
├──────────────────────────────────┤
│      Memory OS AI Server         │
│  ┌────────┐  ┌───────────────┐   │
│  │ FAISS  │  │ Chat Extractor│   │
│  │ Index  │  │ (4 sources)   │   │
│  └────────┘  └───────────────┘   │
│  ┌────────────────────────────┐  │
│  │ Cross-Project Linking      │  │
│  └────────────────────────────┘  │
└──────────────────────────────────┘

Features

21 MCP tools for memory management, search, chat persistence, project linking, and cloud storage
Semantic search with FAISS + SentenceTransformers (all-MiniLM-L6-v2)
Multi-format ingestion: PDF, DOCX, TXT, images (OCR), audio (Whisper), PPTX
Chat extraction: auto-detects Claude, ChatGPT, Copilot, and terminal history
Cross-project linking: share memory across multiple workspaces
Cloud storage overflow: auto-backup to Google Drive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2
3 transports: stdio (default), SSE (--sse), Streamable HTTP (--http)
MCP Resources: memory://documents/*, memory://logs/conversation, memory://linked/*
Local-first: all data on your machine by default, cloud only when disk runs low

21 MCP Tools

Tool	Description
`memory_ingest`	Index a folder of documents into FAISS
`memory_search`	Semantic search across all indexed content
`memory_search_occurrences`	Count keyword occurrences across documents
`memory_get_context`	Get relevant context for the current task
`memory_list_documents`	List all indexed documents with stats
`memory_transcribe`	Transcribe audio files (Whisper)
`memory_status`	Engine status (index size, model, device)
`memory_compact`	Compact/deduplicate the FAISS index
`memory_chat_sync`	Sync messages from configured chat sources
`memory_chat_source_add`	Add a chat source (Claude, ChatGPT, etc.)
`memory_chat_source_remove`	Remove a chat source
`memory_chat_status`	Status of all chat sources
`memory_chat_auto_detect`	Auto-detect chat workspaces on disk
`memory_session_brief`	Full memory briefing for session start
`memory_chat_save`	Persist conversation messages to memory
`memory_project_link`	Link another project's memory
`memory_project_unlink`	Unlink a project
`memory_project_list`	List all linked projects
`memory_cloud_configure`	Configure cloud storage backend for overflow
`memory_cloud_status`	Show local disk + cloud storage status
`memory_cloud_sync`	Push/pull/auto-sync between local and cloud

Quick Start

Prerequisites

Python 3.10+
Optional: tesseract (OCR), ffmpeg (audio), antiword (legacy .doc)

# macOS
brew install tesseract ffmpeg antiword

# Ubuntu/Debian
sudo apt-get install tesseract-ocr ffmpeg antiword

Install

git clone https://github.com/romainsantoli-web/Memory-os-ai.git
cd Memory-os-ai
pip install -e ".[dev,audio]"

Auto-Setup (recommended)

# Setup for your AI client:
memory-os-ai setup claude-code    # Claude Code
memory-os-ai setup codex          # Codex CLI
memory-os-ai setup vscode         # VS Code Copilot
memory-os-ai setup claude-desktop # Claude Desktop
memory-os-ai setup chatgpt        # ChatGPT (manual bridge)
memory-os-ai setup all            # All of the above

# Check status:
memory-os-ai setup status

Manual Start

# stdio (default — Claude Code, VS Code, Codex)
memory-os-ai

# SSE transport (port 8765)
memory-os-ai --sse

# Streamable HTTP (port 8765)
memory-os-ai --http

Project Structure

Memory-os-ai/
├── src/memory_os_ai/
│   ├── __init__.py          # Public API: MemoryEngine, ChatExtractor, TOOL_MODELS
│   ├── __main__.py          # python -m memory_os_ai entry point
│   ├── server.py            # MCP server — 21 tools, 3 transports, resources
│   ├── engine.py            # FAISS engine — indexing, search, compact, session brief
│   ├── cloud_storage.py     # 8 cloud backends (GDrive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2)
│   ├── storage_router.py    # Smart routing: local-first with cloud overflow
│   ├── models.py            # 21 Pydantic models + TOOL_MODELS registry
│   ├── chat_extractor.py    # 4 extractors: Claude, ChatGPT, Copilot, terminal
│   ├── instructions.py      # MEMORY_INSTRUCTIONS for AI clients
│   └── setup.py             # Auto-setup CLI for 5 AI clients
├── bridges/
│   ├── claude-code/         # CLAUDE.md with memory rules
│   ├── claude-desktop/      # config.json for Claude Desktop
│   ├── codex/               # AGENTS.md for Codex CLI
│   ├── vscode/              # mcp.json for VS Code
│   └── chatgpt/             # mcp-connection.json for ChatGPT
├── tests/                   # 410+ tests — 96% coverage
│   ├── test_memory.py       # Engine + models (60 tests)
│   ├── test_chat_extractor.py  # Chat extraction (39 tests)
│   ├── test_bridges.py      # Bridge configs (22 tests)
│   ├── test_gaps.py         # Compact, cross-project, resources (34 tests)
│   ├── test_server_dispatch.py # Server dispatch + async (61 tests)
│   ├── test_setup.py        # Setup CLI targets
│   ├── test_z_coverage_boost.py # Coverage boost (35 tests)
│   └── test_zz_full_coverage.py # Full coverage (97 tests)
├── pyproject.toml           # v3.1.0 — deps, scripts, coverage config + cloud optional deps
├── Dockerfile               # Container deployment
└── README.md

Cloud Storage (v3.1.0)

When local disk runs low (< 500 MB free by default), memory data automatically overflows to a configured cloud backend.

Supported Providers

Provider	Install	Credentials
Google Drive	`pip install memory-os-ai[cloud-gdrive]`	`credentials_json` or `token_json` + `folder_id`
iCloud Drive	(macOS native, no extra deps)	`container` name (default: `memory-os-ai`)
Dropbox	`pip install memory-os-ai[cloud-dropbox]`	`access_token` + `folder`
OneDrive	(auto-detects mount) or Graph API	`mount_path` or `access_token`
Amazon S3	`pip install memory-os-ai[cloud-s3]`	`bucket`, `aws_access_key_id`, `aws_secret_access_key`
Azure Blob	`pip install memory-os-ai[cloud-azure]`	`connection_string` + `container`
Box	`pip install memory-os-ai[cloud-box]`	`access_token` + `folder_id`
Backblaze B2	`pip install memory-os-ai[cloud-b2]`	`application_key_id`, `application_key`, `bucket_name`
All providers	`pip install memory-os-ai[cloud-all]`	—

Usage

# Configure via environment (auto-activates on server start)
export MEMORY_CLOUD_PROVIDER=icloud
export MEMORY_CLOUD_CONFIG='{"container": "memory-os-ai"}'
memory-os-ai

# Or configure at runtime via MCP tool:
#   memory_cloud_configure(provider="s3", credentials={"bucket": "my-bucket", ...})
#   memory_cloud_status()       → local disk + cloud usage
#   memory_cloud_sync("push")   → backup to cloud
#   memory_cloud_sync("pull")   → restore from cloud
#   memory_cloud_sync("auto")   → offload if disk low

Configuration

Environment Variables

Variable	Default	Description
`MEMORY_CACHE_DIR`	`~/.memory-os-ai`	Cache / FAISS index directory
`MEMORY_MODEL`	`all-MiniLM-L6-v2`	SentenceTransformer model name
`MEMORY_API_KEY`	(none)	Optional API key for SSE/HTTP auth
`MEMORY_CLOUD_PROVIDER`	(none)	Cloud provider name (see table above)
`MEMORY_CLOUD_CONFIG`	(none)	JSON credentials or path to JSON file
`MEMORY_DISK_THRESHOLD`	`524288000`	Bytes free before cloud overflow (500 MB)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Run with coverage
python -m pytest tests/ --cov=memory_os_ai --cov-report=term-missing

# Coverage threshold: 80% (enforced in pyproject.toml)

License

GNU Lesser General Public License v3.0 (LGPL-3.0). See LICENSE for details.

For commercial licensing, contact [email protected].

Part of the OpenClaw Ecosystem

Memory OS AI is designed to work alongside the OpenClaw agent infrastructure:

Repo	Description
setup-vs-agent-firm	Factory for AI agent firms — 28 SKILL.md, 5 SOUL.md, 15 sectors
mcp-openclaw-extensions	115 MCP tools — security audit, A2A bridge, fleet management
Memory OS AI (this repo)	Semantic memory + chat persistence — universal MCP bridge

Together they form a complete stack: memory (this repo) → skills & souls (setup-vs-agent-firm) → security & orchestration (mcp-openclaw-extensions).

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

⚠️ Contenu généré par IA — validation humaine requise avant utilisation.